Global patterns in small-scale cannabis growers’ distribution practices: Exploring the grower-distributor nexus
Bibliographic record
Abstract
BACKGROUND: While the supply of cannabis is commonly assumed to be dominated by criminal gangs, a sizable share of the domestic cannabis supply is provided by small-scale growers. This article examines the nature and scope of small-scale growers' distribution practices, with a particular focus on cross-country differences and variations between different types of grower-distributors, i.e., "non-suppliers", "exclusive social suppliers", "sharers and sellers" and "exclusive sellers". METHODS: Based on a large convenience web survey sample of predominantly small-scale cannabis growers from 18 countries, this article draws on data from two subsamples. The first subsample includes past-year growers in all 18 countries who answered questions regarding their market participation (n = 8,812). The second subsample includes past-year growers in 13 countries, who answered additional questions about their supply practices (n = 2,296). RESULTS: The majority of the cannabis growers engaged in distribution of surplus products, making them in effect "grower-distributors". Importantly, many did so as a secondary consequence of growing, and social supply (e.g., sharing and gifting) is much more common than selling. While growers who both shared and sold ("sharers and sellers"), and especially those who only sold ("exclusive sellers"), grew a higher number of plants and were most likely to grow due to a wish to sell for profits, the majority of these are best described as small-scale sellers. That is, the profit motive for growing was often secondary to non-financial motives and most sold to a limited number of persons in their close social network. CONCLUSION: We discuss the implications of the findings on the structural process of import-substitution in low-end cannabis markets, including a growing normalization of cannabis supply.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".